27 Causal Per-protocol Analyses of Vaccine Trials

Oregon Convention Center 

Per-protocol analyses of vaccine efficacy trials typically compare event rates between participants assigned to vaccine and placebo among those who adhered to the trial protocol. However, conditioning on adherence introduces the potential for confounding bias because it occurs post-randomization. In this work, we present the goals of per-protocol analyses in vaccine efficacy trials using the Neyman-Rubin causal model. We define three effects: the intention-to-treat effect, the per-protocol cohort effect, and the causal per-protocol effect. We present the correct interpretation of these three effects, and weigh their pros and cons as effects of interest in the analysis of vaccine trials. We then introduce estimators of these three effects, focusing in particular on estimation of the causal per-protocol effect under a no unobserved confounding assumption using Inverse Probability of Treatment Weighting and Longitudinal Targeted Maximum Likelihood Estimation. We use simulation studies to demonstrate how non-adherence, confounding, and effect modification influence when these estimators can be used to make reliable conclusions about the causal effect of protocol adherence.

Causal Inference

Per-protocol analyses

Vaccine trials

Inverse probability of treatment weighting

Longitudinal targeted maximum likelihood estimation 

Section on Statistics in Epidemiology